{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"#  &#x1F4D1; **作业 12: 强化学习**\n\n如果有任何问题, 可以邮件联系我们 [ntu-ml-2022spring-ta@googlegroups.com](ntu-ml-2022spring-ta@googlegroups.com)","metadata":{}},{"cell_type":"code","source":"# 必要环境配置与包安转\n!apt update\n!pip uninstall gym -y\n!pip install gym==0.25.2\n!apt install python-opengl xvfb -y\n!pip install swig\n!pip install box2d box2d-kengz\n!pip install gym[box2d] pyvirtualdisplay","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-06-14T16:04:06.217774Z","iopub.execute_input":"2023-06-14T16:04:06.218160Z","iopub.status.idle":"2023-06-14T16:05:09.455979Z","shell.execute_reply.started":"2023-06-14T16:04:06.218132Z","shell.execute_reply":"2023-06-14T16:05:09.454500Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"Get:1 http://security.ubuntu.com/ubuntu focal-security InRelease [114 kB]\nGet:2 http://packages.cloud.google.com/apt gcsfuse-focal InRelease [5002 B]    \u001b[0m\nHit:3 https://packages.cloud.google.com/apt cloud-sdk InRelease                \u001b[0m\u001b[33m\nHit:4 http://archive.ubuntu.com/ubuntu focal InRelease0m                       \u001b[0m\u001b[33m\nGet:5 http://archive.ubuntu.com/ubuntu focal-updates InRelease [114 kB]\nGet:6 http://archive.ubuntu.com/ubuntu focal-backports InRelease [108 kB]\nGet:7 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 Packages [1356 kB]\nGet:8 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 Packages [3260 kB]\nFetched 4957 kB in 2s (3043 kB/s) \u001b[0m                \u001b[0m\u001b[33m\u001b[33m\u001b[33m\nReading package lists... Done\nBuilding dependency tree       \nReading state information... Done\n70 packages can be upgraded. Run 'apt list --upgradable' to see them.\nFound existing installation: gym 0.25.2\nUninstalling gym-0.25.2:\n  Successfully uninstalled gym-0.25.2\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0mCollecting gym==0.25.2\n  Using cached gym-0.25.2-py3-none-any.whl\nRequirement already satisfied: numpy>=1.18.0 in /opt/conda/lib/python3.10/site-packages (from gym==0.25.2) (1.23.5)\nRequirement already satisfied: cloudpickle>=1.2.0 in /opt/conda/lib/python3.10/site-packages (from gym==0.25.2) (2.2.1)\nRequirement already satisfied: gym-notices>=0.0.4 in /opt/conda/lib/python3.10/site-packages (from gym==0.25.2) (0.0.8)\nInstalling collected packages: gym\nSuccessfully installed gym-0.25.2\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\nReading package lists... Done\nBuilding dependency tree       \nReading state information... Done\npython-opengl is already the newest version (3.1.0+dfsg-2build1).\nxvfb is already the newest version (2:1.20.13-1ubuntu1~20.04.8).\n0 upgraded, 0 newly installed, 0 to remove and 70 not upgraded.\nRequirement already satisfied: swig in /opt/conda/lib/python3.10/site-packages (4.1.1)\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0mRequirement already satisfied: box2d in /opt/conda/lib/python3.10/site-packages (2.3.2)\nRequirement already satisfied: box2d-kengz in /opt/conda/lib/python3.10/site-packages (2.3.3)\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0mRequirement already satisfied: gym[box2d] in /opt/conda/lib/python3.10/site-packages (0.25.2)\nRequirement already satisfied: pyvirtualdisplay in /opt/conda/lib/python3.10/site-packages (3.0)\nRequirement already satisfied: numpy>=1.18.0 in /opt/conda/lib/python3.10/site-packages (from gym[box2d]) (1.23.5)\nRequirement already satisfied: cloudpickle>=1.2.0 in /opt/conda/lib/python3.10/site-packages (from gym[box2d]) (2.2.1)\nRequirement already satisfied: gym-notices>=0.0.4 in /opt/conda/lib/python3.10/site-packages (from gym[box2d]) (0.0.8)\nRequirement already satisfied: box2d-py==2.3.5 in /opt/conda/lib/python3.10/site-packages (from gym[box2d]) (2.3.5)\nRequirement already satisfied: pygame==2.1.0 in /opt/conda/lib/python3.10/site-packages (from gym[box2d]) (2.1.0)\nRequirement already satisfied: swig==4.* in /opt/conda/lib/python3.10/site-packages (from gym[box2d]) (4.1.1)\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0m","output_type":"stream"}]},{"cell_type":"code","source":"# 加载需要的包\nfrom pyvirtualdisplay import Display\nimport matplotlib.pyplot as plt\nfrom IPython import display\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch import optim\nfrom torch.nn import functional as F\nimport typing as typ\nfrom torch.distributions import Categorical\n# from tqdm.auto import tqdm\nfrom tqdm import tqdm # 下载notebook 仍能显示\nimport gym\nimport random\nimport os\nfrom rich.console import Console\nimport warnings\n\nwarnings.filterwarnings('ignore')\ncs = Console()\nv_dispaly = Display(visible=0, size=(1400, 900))\nv_dispaly.start()\nprint(\"gym.__version__=\", gym.__version__)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T16:05:09.459329Z","iopub.execute_input":"2023-06-14T16:05:09.460180Z","iopub.status.idle":"2023-06-14T16:05:09.622324Z","shell.execute_reply.started":"2023-06-14T16:05:09.460134Z","shell.execute_reply":"2023-06-14T16:05:09.621524Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"gym.__version__= 0.25.2\n","output_type":"stream"}]},{"cell_type":"code","source":"# 设置seed便于复现\nNOTEBOOK_SEED = 543","metadata":{"execution":{"iopub.status.busy":"2023-06-14T16:05:09.625373Z","iopub.execute_input":"2023-06-14T16:05:09.625827Z","iopub.status.idle":"2023-06-14T16:05:09.631531Z","shell.execute_reply.started":"2023-06-14T16:05:09.625794Z","shell.execute_reply":"2023-06-14T16:05:09.630219Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"def all_seed(seed=6666, env=None):\n    if env is not None:\n        env.seed(seed)\n        env.action_space.seed(seed)\n    np.random.seed(seed)\n    random.seed(seed)\n    # CPU\n    torch.manual_seed(seed)\n    # GPU\n    if torch.cuda.is_available():\n        torch.cuda.manual_seed_all(seed)\n        torch.cuda.manual_seed(seed)\n    # python全局\n    os.environ['PYTHONHASHSEED'] = str(seed)\n    # cudnn\n    torch.backends.cudnn.deterministic = True\n    torch.backends.cudnn.benchmark = False\n    torch.backends.cudnn.enabled = False\n    print(f'Set env random_seed = {seed}')","metadata":{"execution":{"iopub.status.busy":"2023-06-14T16:05:09.634882Z","iopub.execute_input":"2023-06-14T16:05:09.635678Z","iopub.status.idle":"2023-06-14T16:05:09.646916Z","shell.execute_reply.started":"2023-06-14T16:05:09.635630Z","shell.execute_reply":"2023-06-14T16:05:09.645668Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"markdown","source":"# 环境描述\n\n“LunarLander-v2”是为了模拟飞船降落在月球表面时的情况。这项任务是使飞船能够“安全”降落在两面黄旗之间的停机坪上。\n> 着陆台始终位于坐标（0,0）处  \n> 坐标是状态向量中的前两个数字\n\n![](https://www.gymlibrary.dev/_images/lunar_lander.gif)\n\n`LunarLander-v2`实际上包含`Agent`和`Environment`.   \n在本作业中，我们将使用函数`step()` 来控制`Agent`的动作。  \n`step()`将返回“环境”给予的观察/状态和奖励. \n- observation / state\n- reward\n- done (True/ False)\n- Other information\n\n`Discrete(4)`意味着`Agent`可以采取四种行动.\n- 0 agnet 不做任何动作\n- 2 将加速向下\n- 1 将加速向左\n- 3 将加速向右\n\n\n接下来，我们将尝试使代理与环境进行交互。在采取任何操作之前，我们建议调用`env.reset()`函数来重置环境。此外，此函数将返回环境的初始状态。    \n具体我们可以在`Random Agent` 中查看\n\n关于奖励`reward`\n> 着陆台始终位于坐标（0,0）处。坐标是状态向量中的前两个数字。  \n> 从屏幕顶部移动到着陆台和零速度的奖励约为100.140分。若着陆器离开着陆台，它将失去回报。  \n> 如果着陆器坠毁或休息，则结束，获得额外的-100或+100分。  \n> 每个腿部接地触点为+10。主机点火为每帧-0.3分。已解决的是200点。","metadata":{}},{"cell_type":"code","source":"def gym_env_desc(env_name):\n    \"\"\"\n    对环境的简单描述\n    \"\"\"\n    env = gym.make(env_name)\n    state_shape = env.observation_space.shape\n    cs.print(\"observation_space:\\n\\t\", env.observation_space)\n    cs.print(\"action_space:\\n\\t\", env.action_space)\n    try:\n        action_shape = env.action_space.n\n        action_type = '离散'\n        extra=''\n    except Exception as e:\n        action_shape = env.action_space.shape\n        low_ = env.action_space.low[0]  # 连续动作的最小值\n        up_ = env.action_space.high[0]  # 连续动作的最大值\n        extra=f'<{low_} -> {up_}>'\n        action_type = '连续'\n    print(f'[ {env_name} ](state: {state_shape},action: {action_shape}({action_type} {extra}))')\n    return ","metadata":{"execution":{"iopub.status.busy":"2023-06-14T16:05:09.648727Z","iopub.execute_input":"2023-06-14T16:05:09.649434Z","iopub.status.idle":"2023-06-14T16:05:09.665183Z","shell.execute_reply.started":"2023-06-14T16:05:09.649387Z","shell.execute_reply":"2023-06-14T16:05:09.663787Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"env_name= 'LunarLander-v2'\ngym_env_desc(env_name)\nenv = gym.make(env_name)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T16:05:09.668763Z","iopub.execute_input":"2023-06-14T16:05:09.669279Z","iopub.status.idle":"2023-06-14T16:05:09.699129Z","shell.execute_reply.started":"2023-06-14T16:05:09.669231Z","shell.execute_reply":"2023-06-14T16:05:09.698269Z"},"trusted":true},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":"observation_space:\n         \u001b[1;35mBox\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m-1.5\u001b[0m       \u001b[1;36m-1.5\u001b[0m       \u001b[1;36m-5\u001b[0m.        \u001b[1;36m-5\u001b[0m.        \u001b[1;36m-3.1415927\u001b[0m \u001b[1;36m-5\u001b[0m.\n \u001b[1;36m-0\u001b[0m.        \u001b[1;36m-0\u001b[0m.       \u001b[1m]\u001b[0m, \u001b[1m[\u001b[0m\u001b[1;36m1.5\u001b[0m       \u001b[1;36m1.5\u001b[0m       \u001b[1;36m5\u001b[0m.        \u001b[1;36m5\u001b[0m.        \u001b[1;36m3.1415927\u001b[0m \u001b[1;36m5\u001b[0m.        \u001b[1;36m1\u001b[0m.\n \u001b[1;36m1\u001b[0m.       \u001b[1m]\u001b[0m, \u001b[1m(\u001b[0m\u001b[1;36m8\u001b[0m,\u001b[1m)\u001b[0m, float32\u001b[1m)\u001b[0m\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">observation_space:\n         <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Box</span><span style=\"font-weight: bold\">([</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-1.5</span>       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-1.5</span>       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-5</span>.        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-5</span>.        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-3.1415927</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-5</span>.\n <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0</span>.        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0</span>.       <span style=\"font-weight: bold\">]</span>, <span style=\"font-weight: bold\">[</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.5</span>       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.5</span>       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span>.        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span>.        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1415927</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span>.        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>.\n <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>.       <span style=\"font-weight: bold\">]</span>, <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span>,<span style=\"font-weight: bold\">)</span>, float32<span style=\"font-weight: bold\">)</span>\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"action_space:\n         \u001b[1;35mDiscrete\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1m)\u001b[0m\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">action_space:\n         <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Discrete</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span><span style=\"font-weight: bold\">)</span>\n</pre>\n"},"metadata":{}},{"name":"stdout","text":"[ LunarLander-v2 ](state: (8,),action: 4(离散 ))\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Random Agent\n最后，在我们开始训练之前，我们可以看看一个`Random Agent`是否能成功登月。","metadata":{}},{"cell_type":"code","source":"env = gym.make(env_name) # 如果gym.__version__==0.26.2: 只需要 gym.make(env_name, render_mode='rgb_array') \nall_seed(seed=NOTEBOOK_SEED, env=env)\nenv.reset()\n\nimg = plt.imshow(env.render(mode='rgb_array')) # 如果gym.__version__==0.26.2: 只需要 plt.imshow(env.render())\ndone = False\nwhile not done:\n    a = env.action_space.sample()\n    n_state, reward, done, _ = env.step(a)\n    img.set_data(env.render(mode='rgb_array')) # 如果gym.__version__==0.26.2: 只需要 img.set_data(env.render()) \n    display.display(plt.gcf())\n    display.clear_output(wait=True)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T12:52:58.064692Z","iopub.execute_input":"2023-06-14T12:52:58.065026Z","iopub.status.idle":"2023-06-14T12:53:10.253204Z","shell.execute_reply.started":"2023-06-14T12:52:58.064998Z","shell.execute_reply":"2023-06-14T12:53:10.251985Z"},"trusted":true},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"# 策略梯度\n现在，我们可以建立一个简单的`Policy Network`。网络将返回动作空间概率，再抽样出一个动作","metadata":{"execution":{"iopub.status.busy":"2023-06-11T06:35:58.595315Z","iopub.execute_input":"2023-06-11T06:35:58.595703Z","iopub.status.idle":"2023-06-11T06:35:58.600202Z","shell.execute_reply.started":"2023-06-11T06:35:58.595674Z","shell.execute_reply":"2023-06-11T06:35:58.599066Z"}}},{"cell_type":"code","source":"class PolicyGradientNet(nn.Module):\n    def __init__(self, state_dim: int, action_dim: int):\n        super(PolicyGradientNet, self).__init__()\n        self.q_net = nn.ModuleList([\n            nn.ModuleDict({\n                'linear': nn.Linear(state_dim, 32),\n                'linear_activation': nn.Tanh()\n            }),\n            nn.ModuleDict({\n                'linear': nn.Linear(32, 32),\n                'linear_activation': nn.Tanh()\n            }),\n            nn.ModuleDict({\n                'linear': nn.Linear(32, action_dim),\n                'linear_activation': nn.Softmax(dim=-1)\n            })\n        ])\n        \n    def forward(self, x):\n        for layer in self.q_net:\n            x = layer['linear_activation'](layer['linear'](x))\n        return x","metadata":{"execution":{"iopub.status.busy":"2023-06-14T16:05:09.700475Z","iopub.execute_input":"2023-06-14T16:05:09.701434Z","iopub.status.idle":"2023-06-14T16:05:09.710319Z","shell.execute_reply.started":"2023-06-14T16:05:09.701357Z","shell.execute_reply":"2023-06-14T16:05:09.709506Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"markdown","source":"## &#x1F4CC; REINFORCE\n\n\\begin{aligned}\n      &\\rule{110mm}{0.4pt}                                                                 \\\\\n      &\\textbf{Algorithm 1}:\\text{策略梯度(Policy Gradient)}                                \\\\\n      &\\rule{110mm}{0.4pt}                                                                 \\\\\n      &\\textbf{function} :\\text{REINFORCE}                         \\\\\n      &\\hspace{5mm}\\text{初始化策略网络参数(Initialize policy parameters)} \\ \\  \\theta \\\\\n      &\\hspace{5mm}\\textbf{for } \\: \\text{episode_transition  in} \\  [\\{s_1, a_1, r_1 \\}, \\{s_2, a_2, r_2 \\}...\\{s_n, a_n, r_n\\}] \\text{~} \\pi(\\theta) \\ \\textbf{do}                         \\\\\n      &\\hspace{10mm}\\textbf{for } t=1 \\text{ to T } \\textbf{ do}\\: \\\\\n      &\\hspace{15mm}\\text{计算折扣收益} R_t = \\sum_{i=t}^{T} \\gamma ^{i-t} r_i \\\\\n      &\\hspace{15mm}\\text{更新策略网络参数} \\theta \\leftarrow \\theta + \\alpha \\nabla_{\\theta} log \\pi_{\\theta}(a_t|s_t) R_t \\\\\n      &\\hspace{15mm}\\textbf{end for } \\\\\n      &\\hspace{10mm}\\textbf{end for } \\\\\n      &\\hspace{10mm}\\bf{return} \\:  \\theta                                                 \\\\\n      &\\textbf{end function} \\\\\n      &\\rule{110mm}{0.4pt}                                                          \\\\[-1.ex]\n \\end{aligned}\n\n","metadata":{}},{"cell_type":"code","source":"from torch.optim.lr_scheduler import StepLR\nfrom typing import List, Dict, AnyStr\nfrom functools import reduce\n\nclass REINFORCE():\n    def __init__(self, state_dim: int, action_dim: int, lr: float=0.001, gamma: float=0.9, \n                 stepLR_step_size:int = 200,\n                 stepLR_gamma:float = 0.1,\n                 normalize_reward:bool=False):\n        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n        self.policy_net = PolicyGradientNet(state_dim, action_dim)\n        self.policy_net.to(self.device)\n        self.opt = optim.Adam(self.policy_net.parameters(), lr=lr)\n        self.stepLR_step_size = stepLR_step_size\n        self.stepLR_gamma = stepLR_gamma\n        self.sche = StepLR(self.opt, step_size=self.stepLR_step_size, gamma=self.stepLR_gamma)\n        self.gamma = gamma\n        self.normalize_reward = normalize_reward\n        self.training = True\n    \n    def train(self):\n        self.training = True\n        self.policy_net.train()\n\n    def eval(self):\n        self.training = False\n        self.policy_net.eval()\n        \n    @torch.no_grad()\n    def policy(self, state):\n        \"\"\"\n        sample action\n        \"\"\"\n        action_prob = self.policy_net(torch.FloatTensor(state).to(self.device))\n        action_dist = Categorical(action_prob)\n        action = action_dist.sample()\n        return action.detach().cpu().item()\n\n    def batch_update(self, batch_episode: List[Dict[AnyStr, List]]):\n        for transition_dict in batch_episode:\n            self.update(transition_dict)\n        self.sche.step()\n\n    def update(self, transition_dict):\n        reward_list = transition_dict['rewards']\n        state_list = transition_dict['states']\n        action_list = transition_dict['actions']\n        # 分数 normalize\n        if self.normalize_reward:\n            reward_list = (np.array(reward_list) - np.mean(reward_list)) / (np.std(reward_list) + 1e-9)\n        Rt = 0\n        self.opt.zero_grad()\n        for i in reversed(range(len(reward_list))):  # 从最后一步算起\n            reward = reward_list[i]\n            state = torch.tensor([state_list[i]], dtype=torch.float).to(self.device)\n            action = torch.tensor([action_list[i]]).unsqueeze(0).to(self.device)\n            log_prob = torch.log(self.policy_net(state).gather(1, action.long()))\n            # Rt = \\sum_{i=t}^T \\gamma ^ {i-t} r_i\n            Rt = self.gamma * Rt + reward\n            loss = -log_prob * Rt\n            loss.backward()\n        self.opt.step() \n        \n    def save_model(self, model_dir):\n        if not os.path.exists(model_dir):\n            os.makedirs(model_dir)\n        file_path = os.path.join(model_dir, 'policy_net.ckpt')\n        torch.save(self.policy_net.state_dict(), file_path)\n\n    def load_model(self, model_dir):\n        file_path = os.path.join(model_dir, 'policy_net.ckpt')\n        self.policy_net.load_state_dict(torch.load(file_path))\n","metadata":{"execution":{"iopub.status.busy":"2023-06-14T16:05:09.712106Z","iopub.execute_input":"2023-06-14T16:05:09.712441Z","iopub.status.idle":"2023-06-14T16:05:09.736363Z","shell.execute_reply.started":"2023-06-14T16:05:09.712413Z","shell.execute_reply":"2023-06-14T16:05:09.735182Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"markdown","source":"##  &#x2728; 训练 Angent\n用策略梯度算法训练Agent","metadata":{}},{"cell_type":"code","source":"def train_on_policy(\n    agent, \n    env, \n    num_batch=450,\n    random_batch=2,\n    episode_per_batch=3,\n    episode_max_step=300,\n    save_mdoel_dir='./check_point'\n):\n    \"\"\"\n    on policy 强化学习算法学习简单函数\n    params:\n        agent: 智能体\n        env: 环境\n        random_batch: 前N个batch用random Agent收集数据\n        num_batch: 训练多少个batch\n        episode_per_batch： 一个batch下多少个episode\n        episode_max_step: 每个episode最大步数\n        save_mdoel_dir: 模型保存的文件夹\n    \"\"\"\n    EPISODE_PER_BATCH = episode_per_batch\n    NUM_BATCH = num_batch\n    RANDOM_BATCH = random_batch\n    MAX_STEP = episode_max_step\n    avg_total_rewards, avg_final_rewards, avg_total_steps = [], [], []\n    agent.train()\n    tq_bar = tqdm(range(RANDOM_BATCH + NUM_BATCH))\n    recent_best = -np.inf\n    batch_best = -np.inf\n    all_rewards = []\n    for batch in tq_bar:\n        tq_bar.set_description(f\"[ {batch+1}/{NUM_BATCH} ]\")\n        batch_recordes = []\n        total_rewards = []\n        total_steps = []\n        final_rewards = []\n        for ep in range(EPISODE_PER_BATCH):\n            rec_dict = {'states': [], 'actions': [], 'next_states': [], 'rewards': [], 'dones': []}\n            state = env.reset()\n            total_reward, total_step = 0, 0\n            while True:\n                a = agent.policy(state)\n                if batch < RANDOM_BATCH:\n                    a = env.action_space.sample()\n\n                n_state, reward, done, _ = env.step(a)\n                # 收集每一步的信息\n                rec_dict['states'].append(state)\n                rec_dict['actions'].append(a)\n                rec_dict['next_states'].append(n_state)\n                rec_dict['rewards'].append(reward)\n                rec_dict['dones'].append(done)\n                state = n_state\n                total_reward += reward\n                total_step += 1\n                if done or total_step > MAX_STEP:\n                    # 一个episode结束后 收集相关信息\n                    final_rewards.append(reward)\n                    total_steps.append(total_step)\n                    total_rewards.append(total_reward)\n                    all_rewards.append(total_reward)\n                    batch_recordes.append(rec_dict)\n                    break\n\n        avg_total_reward = sum(total_rewards) / len(total_rewards)\n        avg_final_reward = sum(final_rewards) / len(final_rewards)\n        avg_total_step = sum(total_steps) / len(total_steps)\n        recent_batch_best = np.mean(all_rewards[-10:])\n        avg_total_rewards.append(avg_total_reward)\n        avg_final_rewards.append(avg_final_reward)\n        avg_total_steps.append(avg_total_step)\n        # 在进度条后面显示关注的信息\n        tq_bar.set_postfix({\n            \"Total\": f\"{avg_total_reward: 4.1f}\", \n            \"Recent\": f\"{recent_batch_best: 4.1f}\", \n            \"RecentBest\": f\"{recent_best: 4.1f}\", \n            \"Final\": f\"{avg_final_reward: 4.1f}\", \n            \"Steps\": f\"{avg_total_step: 4.1f}\"})\n        agent.batch_update(batch_recordes)\n        if avg_total_reward > batch_best and (batch > 4 + RANDOM_BATCH):\n            batch_best = avg_total_reward\n            agent.save_model(save_mdoel_dir + \"_batchBest\")\n        if recent_batch_best > recent_best and (batch > 4 + RANDOM_BATCH):\n            recent_best = recent_batch_best\n            agent.save_model(save_mdoel_dir)\n        \n    return avg_total_rewards, avg_final_rewards, avg_total_steps","metadata":{"execution":{"iopub.status.busy":"2023-06-14T16:05:09.738474Z","iopub.execute_input":"2023-06-14T16:05:09.739240Z","iopub.status.idle":"2023-06-14T16:05:09.759818Z","shell.execute_reply.started":"2023-06-14T16:05:09.739191Z","shell.execute_reply":"2023-06-14T16:05:09.758748Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"markdown","source":"### 用简单环境（`CartPole-v1`）测试\n\n看我们的算法写是否正确","metadata":{}},{"cell_type":"code","source":"# test sample env\nenv_name= 'CartPole-v1'\ngym_env_desc(env_name)\nenv = gym.make(env_name)\nall_seed(seed=NOTEBOOK_SEED, env=env)\nagent = REINFORCE(\n    state_dim=env.observation_space.shape[0], \n    action_dim=env.action_space.n, \n    lr=0.01,\n    gamma=0.8,\n    stepLR_step_size=80\n)\navg_total_rewards, avg_final_rewards, avg_total_steps = train_on_policy(\n    agent, env, \n    num_batch=160,\n    random_batch=2,\n    episode_per_batch=5,\n    episode_max_step=300,\n    save_mdoel_dir='./check_point'\n)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T12:53:10.317515Z","iopub.execute_input":"2023-06-14T12:53:10.317957Z","iopub.status.idle":"2023-06-14T12:55:45.170121Z","shell.execute_reply.started":"2023-06-14T12:53:10.317923Z","shell.execute_reply":"2023-06-14T12:55:45.168960Z"},"trusted":true},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":"observation_space:\n         \u001b[1;35mBox\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m-4.8000002e+00\u001b[0m \u001b[1;36m-3.4028235e+38\u001b[0m \u001b[1;36m-4.1887903e-01\u001b[0m \u001b[1;36m-3.4028235e+38\u001b[0m\u001b[1m]\u001b[0m, \u001b[1m[\u001b[0m\u001b[1;36m4.8000002e+00\u001b[0m \u001b[1;36m3.4028235e+38\u001b[0m \n\u001b[1;36m4.1887903e-01\u001b[0m \u001b[1;36m3.4028235e+38\u001b[0m\u001b[1m]\u001b[0m, \u001b[1m(\u001b[0m\u001b[1;36m4\u001b[0m,\u001b[1m)\u001b[0m, float32\u001b[1m)\u001b[0m\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">observation_space:\n         <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Box</span><span style=\"font-weight: bold\">([</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-4.8000002e+00</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-3.4028235e+38</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-4.1887903e-01</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-3.4028235e+38</span><span style=\"font-weight: bold\">]</span>, <span style=\"font-weight: bold\">[</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4.8000002e+00</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.4028235e+38</span> \n<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4.1887903e-01</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.4028235e+38</span><span style=\"font-weight: bold\">]</span>, <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span>,<span style=\"font-weight: bold\">)</span>, float32<span style=\"font-weight: bold\">)</span>\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"action_space:\n         \u001b[1;35mDiscrete\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1m)\u001b[0m\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">action_space:\n         <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Discrete</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"font-weight: bold\">)</span>\n</pre>\n"},"metadata":{}},{"name":"stdout","text":"[ CartPole-v1 ](state: (4,),action: 2(离散 ))\nSet env random_seed = 543\n","output_type":"stream"},{"name":"stderr","text":"[ 162/160 ]: 100%|██████████| 162/162 [02:34<00:00,  1.05it/s, Total=292.4, Recent=287.1, RecentBest=301.0, Final=1.0, Steps=292.4]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### 训练结果查看\n在训练过程中，我们记录了`avg_total_reward`，它表示更新策略网络训练过程中的平均总奖励。\n从理论上讲，如果`Agent`变得更好，则`avg_tal_reward`将增加。","metadata":{}},{"cell_type":"code","source":"fig, axes = plt.subplots(1, 2, figsize=(16, 4))\naxes[0].plot(avg_total_rewards, label='Total Rewards')\naxes[0].plot(avg_final_rewards, label='Final Rewards')\naxes[0].set_title(f'{env_name} - Rewards Curve')\naxes[0].legend()\naxes[1].plot(avg_total_steps, label='steps')\naxes[1].set_title(f'{env_name} - Steps Curve')\naxes[1].legend()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-14T12:55:45.171930Z","iopub.execute_input":"2023-06-14T12:55:45.172273Z","iopub.status.idle":"2023-06-14T12:55:45.752381Z","shell.execute_reply.started":"2023-06-14T12:55:45.172244Z","shell.execute_reply":"2023-06-14T12:55:45.751269Z"},"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1600x400 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"## &#x2728;  训练`LunarLander-v2`","metadata":{}},{"cell_type":"code","source":"env_name= 'LunarLander-v2'\ngym_env_desc(env_name)\nenv = gym.make(env_name)\nall_seed(seed=NOTEBOOK_SEED, env=env)\nagent = REINFORCE(\n    state_dim=env.observation_space.shape[0], \n    action_dim=env.action_space.n, \n    lr=0.001,\n    gamma=0.99,\n    normalize_reward=True,\n    stepLR_step_size = 150,\n    stepLR_gamma = 0.75\n)\n\navg_total_rewards, avg_final_rewards, avg_total_steps = train_on_policy(\n    agent, env, \n    num_batch=800,\n    random_batch=3,\n    episode_per_batch=3,\n    episode_max_step=300, \n    save_mdoel_dir='./check_point_LunarLander_REINFORCE'\n)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T12:56:00.158975Z","iopub.execute_input":"2023-06-14T12:56:00.159368Z","iopub.status.idle":"2023-06-14T13:05:59.634493Z","shell.execute_reply.started":"2023-06-14T12:56:00.159339Z","shell.execute_reply":"2023-06-14T13:05:59.633316Z"},"trusted":true},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":"observation_space:\n         \u001b[1;35mBox\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m-1.5\u001b[0m       \u001b[1;36m-1.5\u001b[0m       \u001b[1;36m-5\u001b[0m.        \u001b[1;36m-5\u001b[0m.        \u001b[1;36m-3.1415927\u001b[0m \u001b[1;36m-5\u001b[0m.\n \u001b[1;36m-0\u001b[0m.        \u001b[1;36m-0\u001b[0m.       \u001b[1m]\u001b[0m, \u001b[1m[\u001b[0m\u001b[1;36m1.5\u001b[0m       \u001b[1;36m1.5\u001b[0m       \u001b[1;36m5\u001b[0m.        \u001b[1;36m5\u001b[0m.        \u001b[1;36m3.1415927\u001b[0m \u001b[1;36m5\u001b[0m.        \u001b[1;36m1\u001b[0m.\n \u001b[1;36m1\u001b[0m.       \u001b[1m]\u001b[0m, \u001b[1m(\u001b[0m\u001b[1;36m8\u001b[0m,\u001b[1m)\u001b[0m, float32\u001b[1m)\u001b[0m\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">observation_space:\n         <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Box</span><span style=\"font-weight: bold\">([</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-1.5</span>       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-1.5</span>       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-5</span>.        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-5</span>.        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-3.1415927</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-5</span>.\n <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0</span>.        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0</span>.       <span style=\"font-weight: bold\">]</span>, <span style=\"font-weight: bold\">[</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.5</span>       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.5</span>       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span>.        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span>.        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1415927</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span>.        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>.\n <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>.       <span style=\"font-weight: bold\">]</span>, <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span>,<span style=\"font-weight: bold\">)</span>, float32<span style=\"font-weight: bold\">)</span>\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"action_space:\n         \u001b[1;35mDiscrete\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1m)\u001b[0m\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">action_space:\n         <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Discrete</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span><span style=\"font-weight: bold\">)</span>\n</pre>\n"},"metadata":{}},{"name":"stdout","text":"[ LunarLander-v2 ](state: (8,),action: 4(离散 ))\nSet env random_seed = 543\n","output_type":"stream"},{"name":"stderr","text":"[ 803/800 ]: 100%|██████████| 803/803 [09:59<00:00,  1.34it/s, Total=50.5, Recent=82.0, RecentBest=128.5, Final=-34.6, Steps=265.7]      \n","output_type":"stream"}]},{"cell_type":"markdown","source":"### 训练结果查看\n在训练过程中，我们记录了`avg_total_reward`，它表示更新策略网络训练过程中的平均总奖励。\n从理论上讲，如果`Agent`变得更好，则`avg_tal_reward`将增加。","metadata":{"execution":{"iopub.status.busy":"2023-06-11T07:18:26.101732Z","iopub.execute_input":"2023-06-11T07:18:26.102122Z","iopub.status.idle":"2023-06-11T07:18:26.108514Z","shell.execute_reply.started":"2023-06-11T07:18:26.102091Z","shell.execute_reply":"2023-06-11T07:18:26.107228Z"}}},{"cell_type":"code","source":"fig, axes = plt.subplots(1, 2, figsize=(16, 4))\naxes[0].plot(avg_total_rewards, label='Total Rewards')\naxes[0].plot(avg_final_rewards, label='Final Rewards')\naxes[0].set_title(f'{env_name} - Rewards Curve')\naxes[0].legend()\naxes[1].plot(avg_total_steps, label='steps')\naxes[1].set_title(f'{env_name} - Steps Curve')\naxes[1].legend()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-14T13:05:59.636896Z","iopub.execute_input":"2023-06-14T13:05:59.637242Z","iopub.status.idle":"2023-06-14T13:06:00.453552Z","shell.execute_reply.started":"2023-06-14T13:05:59.637213Z","shell.execute_reply":"2023-06-14T13:06:00.452070Z"},"trusted":true},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1600x400 with 2 Axes>","image/png":"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&#x2728; Testing\n测试结果将是5次测试的平均奖励","metadata":{}},{"cell_type":"code","source":"def test_agent(agent, env, max_steps=None, plot_flag=True):\n    NUM_OF_TEST = 5 # Do not revise this !!!\n    test_total_reward = []\n    action_list = []\n    env_name_ = str(env).rsplit('<', 1)[-1].replace('>', '')\n    for i in range(NUM_OF_TEST):\n        actions = []\n        state = env.reset()\n        if plot_flag:\n            img = plt.imshow(env.render(mode='rgb_array'))\n            plt.title(f'Test {env_name_}\\n[ {i+1} / {NUM_OF_TEST} ]')\n        total_reward = 0\n        print(f\"Inintial rewards={total_reward}\")\n        done = False\n        ep_step = 0\n        while not done:\n            action = agent.policy(state)\n            actions.append(action)\n            state, reward, done, _ = env.step(action)\n            total_reward += reward\n            ep_step += 1\n            if plot_flag:\n                img.set_data(env.render(mode='rgb_array'))\n                display.display(plt.gcf())\n                display.clear_output(wait=True)\n            if max_steps is not None:\n                done = max_steps < ep_step\n\n        print(f'[ {i+1}/{NUM_OF_TEST} ] step: {ep_step} reward: {total_reward}')\n        test_total_reward.append(total_reward)\n        action_list.append(actions) # save the result of testing \n    return test_total_reward, action_list","metadata":{"execution":{"iopub.status.busy":"2023-06-14T16:35:24.043767Z","iopub.execute_input":"2023-06-14T16:35:24.044982Z","iopub.status.idle":"2023-06-14T16:35:24.056820Z","shell.execute_reply.started":"2023-06-14T16:35:24.044933Z","shell.execute_reply":"2023-06-14T16:35:24.055535Z"},"trusted":true},"execution_count":45,"outputs":[]},{"cell_type":"code","source":"env_name= 'LunarLander-v2'\nall_seed(seed=NOTEBOOK_SEED, env=env)\nagent.load_model('./check_point_LunarLander_REINFORCE')\nagent.eval() \ntest_total_reward, action_list = test_agent(agent, env, max_steps=200)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T13:07:39.877747Z","iopub.execute_input":"2023-06-14T13:07:39.878210Z","iopub.status.idle":"2023-06-14T13:13:14.498328Z","shell.execute_reply.started":"2023-06-14T13:07:39.878178Z","shell.execute_reply":"2023-06-14T13:13:14.496995Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"[ 5/5 ] step: 201\n-1283.0784446862065\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"print(test_total_reward)\nprint(np.mean(test_total_reward))\nprint(\"Action list looks like \", action_list)\nprint(\"Action list's shape looks like \", np.shape(action_list))","metadata":{"execution":{"iopub.status.busy":"2023-06-14T13:20:43.612982Z","iopub.execute_input":"2023-06-14T13:20:43.613463Z","iopub.status.idle":"2023-06-14T13:20:43.620665Z","shell.execute_reply.started":"2023-06-14T13:20:43.613428Z","shell.execute_reply":"2023-06-14T13:20:43.619786Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"[142.26464450207197, 93.08811839133789, 135.46075436962914, 61.85014410047371, 16.921555313793576]\n89.91704333546126\nAction list looks like  [[0 2 2 ... 1 2 0]\n [3 0 3 ... 2 2 1]\n [1 2 2 ... 3 3 0]\n [1 0 1 ... 2 1 1]\n [2 3 3 ... 3 2 3]]\nAction list's shape looks like  (5, 201)\n","output_type":"stream"}]},{"cell_type":"code","source":"distribution = {}\nfor actions in action_list:\n    for action in actions:\n        if action not in distribution.keys():\n            distribution[action] = 1\n        else:\n            distribution[action] += 1\nprint(distribution)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T13:20:46.576494Z","iopub.execute_input":"2023-06-14T13:20:46.576936Z","iopub.status.idle":"2023-06-14T13:20:46.585540Z","shell.execute_reply.started":"2023-06-14T13:20:46.576901Z","shell.execute_reply":"2023-06-14T13:20:46.584141Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":"{0: 91, 2: 525, 1: 202, 3: 187}\n","output_type":"stream"}]},{"cell_type":"markdown","source":"保存模型结果","metadata":{}},{"cell_type":"code","source":"PATH = \"Action_List.npy\"\nnp.save(PATH, np.array(action_list)) \n\n### 这是需要提交的文件！！！ 将测试结果下载到您的设备\n# from google.colab import files\n# files.download(PATH)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T13:20:49.881847Z","iopub.execute_input":"2023-06-14T13:20:49.883088Z","iopub.status.idle":"2023-06-14T13:20:49.889937Z","shell.execute_reply.started":"2023-06-14T13:20:49.883037Z","shell.execute_reply":"2023-06-14T13:20:49.889083Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"markdown","source":"## 服务器(Server)\n\n下面的代码模拟了法官服务器上的环境。可用于测试。","metadata":{}},{"cell_type":"code","source":"action_list = np.load(PATH,allow_pickle=True) # The action list you upload\nenv_name= 'LunarLander-v2'\nall_seed(seed=NOTEBOOK_SEED, env=env)\n\ntest_total_reward = []\nif len(action_list) != 5:\n    print(\"Wrong format of file !!!\")\n    exit(0)\nfor idx, actions in enumerate(action_list):\n    state = env.reset()\n#     img = plt.imshow(env.render(mode='rgb_array'))\n#     plt.title(f'Test {env_name}\\n[ {idx+1} / 5 ]')\n    total_reward = 0\n    done = False\n    for action in actions:\n        state, reward, done, _ = env.step(action)\n        total_reward += reward\n#         img.set_data(env.render(mode='rgb_array'))\n#         display.display(plt.gcf())\n#         display.clear_output(wait=True)\n        if done:\n            break\n    print(f\"Your reward is : %.2f\" % total_reward)\n    test_total_reward.append(total_reward)\n\n# 最终分数\nprint(f\"Your final reward is : %.2f\" % np.mean(test_total_reward))","metadata":{"execution":{"iopub.status.busy":"2023-06-14T13:20:52.915824Z","iopub.execute_input":"2023-06-14T13:20:52.917047Z","iopub.status.idle":"2023-06-14T13:20:53.263046Z","shell.execute_reply.started":"2023-06-14T13:20:52.917001Z","shell.execute_reply":"2023-06-14T13:20:53.261615Z"},"trusted":true},"execution_count":23,"outputs":[{"name":"stdout","text":"Set env random_seed = 543\nYour reward is : 142.26\nYour reward is : 93.09\nYour reward is : 135.46\nYour reward is : 61.85\nYour reward is : 16.92\nYour final reward is : 89.92\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# &#x1F4CC; Actor-Critic\n\nactor 直接使用 `PolicyGradientNet`\n\n\n\\begin{aligned}\n      &\\rule{110mm}{0.4pt}                                                                 \\\\\n      &\\textbf{Algorithm 2}:\\text{Actor-Critic}                   \\\\\n      &\\rule{110mm}{0.4pt}                                                                 \\\\\n      &\\textbf{function} :\\text{REINFORCE with Baseline}                         \\\\\n      &\\hspace{5mm}\\text{初始化策略网络参数(Initialize policy parameters)} \\ \\  \\theta \\\\\n      &\\hspace{5mm}\\text{初始化crtic网络参数(Initialize baseline function parameters)} \\ \\  \\phi \\\\\n      &\\hspace{5mm}\\textbf{for } \\: \\text{episode_transition  in} \\  [\\{s_1, a_1, r_1 \\}, \\{s_2, a_2, r_2 \\}...\\{s_n, a_n, r_n\\}] \\text{~} \\pi(\\theta) \\ \\textbf{do}                         \\\\\n      &\\hspace{10mm}\\textbf{for } t=1 \\text{ to T } \\textbf{ do}\\: \\\\\n      &\\hspace{15mm}\\text{计算折扣收益} R_t = \\sum_{i=t}^{T} \\gamma ^{i-t} r_i \\\\\n      &\\hspace{15mm}\\text{估计优势} A_t = R_t - Critic_{\\phi}(s_t)     \\\\\n      &\\hspace{15mm}\\text{计算critic网络损失} loss_{critic} = ||Critic_{\\phi}(s_t) - R_t||^2     \\\\\n      &\\hspace{15mm}\\text{更新critic网络参数} \\phi \\leftarrow \\phi + \\alpha_{critic} \\nabla_{\\phi} loss_{critic} \\\\\n      &\\hspace{15mm}\\text{更新策略网络参数} \\theta \\leftarrow \\theta + \\alpha \\nabla_{\\theta} log \\pi_{\\theta}(a_t|s_t) R_t \\\\\n      &\\hspace{15mm}\\textbf{end for } \\\\\n      &\\hspace{10mm}\\textbf{end for } \\\\\n      &\\hspace{10mm}\\bf{return} \\:  \\theta                                                 \\\\\n      &\\textbf{end function} \\\\\n      &\\rule{110mm}{0.4pt}                                                          \\\\[-1.ex]\n \\end{aligned}\n","metadata":{"execution":{"iopub.status.busy":"2023-06-03T16:31:25.161863Z","iopub.execute_input":"2023-06-03T16:31:25.162250Z","iopub.status.idle":"2023-06-03T16:31:28.578162Z","shell.execute_reply.started":"2023-06-03T16:31:25.162218Z","shell.execute_reply":"2023-06-03T16:31:28.577136Z"}}},{"cell_type":"code","source":"class ValueNet(torch.nn.Module):\n    def __init__(self, state_dim):\n        super(ValueNet, self).__init__()\n        self.v_net = nn.ModuleList([\n            nn.ModuleDict({\n                'linear': nn.Linear(state_dim, 32),\n                'linear_activation': nn.ReLU()\n            }),\n            nn.ModuleDict({\n                'linear': nn.Linear(32, 32),\n                'linear_activation': nn.ReLU()\n            })\n        ])\n        self.head = nn.Linear(32, 1)\n\n    def forward(self, x):\n        for layer in self.v_net:\n            x = layer['linear_activation'](layer['linear'](x))\n        return self.head(x)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T16:05:09.781514Z","iopub.execute_input":"2023-06-14T16:05:09.782524Z","iopub.status.idle":"2023-06-14T16:05:09.797747Z","shell.execute_reply.started":"2023-06-14T16:05:09.782483Z","shell.execute_reply":"2023-06-14T16:05:09.796323Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"from torch.optim.lr_scheduler import LinearLR\n\nclass ActorCritic:\n    \"\"\"\n    REINFORCE with Baseline\n    \"\"\"\n    def __init__(self, state_dim: int, action_dim: int, actor_lr: float=0.001, critic_lr: float=0.001, gamma: float=0.9, \n                 schedule_kwargs: dict = dict(\n                     schedule_func_name='StepLR',\n                     stepLR_step_size = 200,\n                     stepLR_gamma = 0.1,\n                 ),\n                 normalize_reward:bool = False):\n        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n        self.actor = PolicyGradientNet(state_dim, action_dim)\n        self.critic = ValueNet(state_dim)\n        self.actor.to(self.device)\n        self.critic.to(self.device)\n\n        self.actor_opt = optim.Adam(self.actor.parameters(), lr=actor_lr)\n        self.critic_opt = optim.Adam(self.critic.parameters(), lr=critic_lr)\n        if schedule_kwargs['schedule_func_name'] == \"StepLR\":\n            self.stepLR_step_size = schedule_kwargs['stepLR_step_size']\n            self.stepLR_gamma = schedule_kwargs['stepLR_gamma']\n            self.actor_sche = StepLR(self.actor_opt, step_size=self.stepLR_step_size, gamma=self.stepLR_gamma)\n            self.critic_sche = StepLR(self.critic_opt, step_size=self.stepLR_step_size, gamma=self.stepLR_gamma)\n        if schedule_kwargs['schedule_func_name'] == \"LinearLR\":\n            self.end_factor = schedule_kwargs['end_factor']\n            self.total_iters = schedule_kwargs['total_iters']\n            self.actor_sche = LinearLR(self.actor_opt, start_factor=1, end_factor=self.end_factor, total_iters=self.total_iters)\n            self.critic_sche = LinearLR(self.critic_opt, start_factor=1, end_factor=self.end_factor, total_iters=self.total_iters)\n        \n        self.gamma = gamma\n        self.normalize_reward = normalize_reward\n        self.training = True\n        \n    def train(self):\n        self.training = True\n        self.actor.train()\n        self.critic.train()\n\n    def eval(self):\n        self.training = False\n        self.actor.eval()\n        self.critic.eval()\n        \n    @torch.no_grad()\n    def policy(self, state):\n        \"\"\"\n        sample action\n        \"\"\"\n        action_prob = self.actor(torch.FloatTensor(state).to(self.device))\n        action_dist = Categorical(action_prob)\n        action = action_dist.sample()\n        return action.detach().cpu().item()\n\n    def batch_update(self, batch_episode: List[Dict[AnyStr, List]]):\n        for transition_dict in batch_episode:\n            self.update(transition_dict)\n        self.actor_sche.step()\n        self.critic_sche.step()\n\n    def update(self, transition_dict):\n        reward_list = transition_dict['rewards']\n        state_list = transition_dict['states']\n        action_list = transition_dict['actions']\n        # 分数 normalize\n        if self.normalize_reward:\n            reward_list = (np.array(reward_list) - np.mean(reward_list)) / (np.std(reward_list) + 1e-9)\n        Rt = 0\n        self.actor_opt.zero_grad()\n        self.critic_opt.zero_grad()\n        for i in reversed(range(len(reward_list))):  # 从最后一步算起\n            reward = reward_list[i]\n            state = torch.tensor([state_list[i]], dtype=torch.float).to(self.device)\n            action = torch.tensor([action_list[i]]).unsqueeze(0).to(self.device)\n            log_prob = torch.log(self.actor(state).gather(1, action.long()))\n            # Rt = \\sum_{i=t}^T \\gamma ^ {i-t} r_i\n            Rt = self.gamma * Rt + reward\n            At = Rt - self.critic(state)\n            critic_loss = torch.square(-At)\n            loss = -log_prob * At.detach()  \n            critic_loss.backward() \n            loss.backward()\n        self.actor_opt.step() \n        self.critic_opt.step()\n    \n    def save_model(self, model_dir):\n        if not os.path.exists(model_dir):\n            os.makedirs(model_dir)\n        file_path = os.path.join(model_dir, 'actor.ckpt')\n        torch.save(self.actor.state_dict(), file_path)\n        file_path = os.path.join(model_dir, 'critic.ckpt')\n        torch.save(self.critic.state_dict(), file_path)\n\n    def load_model(self, model_dir):\n        file_path = os.path.join(model_dir, 'actor.ckpt')\n        self.actor.load_state_dict(torch.load(file_path))\n        file_path = os.path.join(model_dir, 'critic.ckpt')\n        self.critic.load_state_dict(torch.load(file_path))","metadata":{"execution":{"iopub.status.busy":"2023-06-14T16:05:09.799587Z","iopub.execute_input":"2023-06-14T16:05:09.800272Z","iopub.status.idle":"2023-06-14T16:05:09.832104Z","shell.execute_reply.started":"2023-06-14T16:05:09.800223Z","shell.execute_reply":"2023-06-14T16:05:09.830399Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"markdown","source":"##  &#x2728; 训练 Angent\n用Actor-critic算法训练Agent","metadata":{}},{"cell_type":"code","source":"env_name= 'LunarLander-v2'\ngym_env_desc(env_name)\nenv = gym.make(env_name)\nall_seed(seed=NOTEBOOK_SEED, env=env)\n\nagent = ActorCritic(\n    state_dim=env.observation_space.shape[0], \n    action_dim=env.action_space.n, \n    actor_lr=0.0025,\n    critic_lr=0.0025, # 0.0017 -> 164   0.0025->263 rd 113\n    gamma=0.99,\n    normalize_reward=True,\n#     schedule_kwargs=dict(\n#          schedule_func_name='StepLR',\n#     #     stepLR_step_size = 150,  # lr=0.001 episode_max_step=300\n#     #     stepLR_gamma = 0.8  # StepLR | Your final reward is : 142.25\n#         stepLR_step_size = 120, #  lr=0.001 episode_max_step=300\n#         stepLR_gamma = 0.85 # StepLR | Your final reward is : 150.42     Recent=105.5, RecentBest=170.4, Final=-0.1, Steps=301.0]  \n#      )\n    schedule_kwargs=dict(\n        schedule_func_name='LinearLR', \n#         end_factor = 0.1,\n#         total_iters = 1000  # Your final reward is : 152.00\n        end_factor = 0.1,\n        total_iters = 1000\n     )\n)\navg_total_rewards, avg_final_rewards, avg_total_step = train_on_policy(\n    agent, env, \n    num_batch=1200,\n    random_batch=3,\n    episode_per_batch=3, \n    episode_max_step=300, # 300\n    save_mdoel_dir='./check_point_LunarLander-ActorCritic2'\n)","metadata":{"execution":{"iopub.status.busy":"2023-06-14T15:37:09.771701Z","iopub.execute_input":"2023-06-14T15:37:09.772059Z","iopub.status.idle":"2023-06-14T16:01:43.771142Z","shell.execute_reply.started":"2023-06-14T15:37:09.772028Z","shell.execute_reply":"2023-06-14T16:01:43.769555Z"},"trusted":true},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":"observation_space:\n         \u001b[1;35mBox\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m-1.5\u001b[0m       \u001b[1;36m-1.5\u001b[0m       \u001b[1;36m-5\u001b[0m.        \u001b[1;36m-5\u001b[0m.        \u001b[1;36m-3.1415927\u001b[0m \u001b[1;36m-5\u001b[0m.\n \u001b[1;36m-0\u001b[0m.        \u001b[1;36m-0\u001b[0m.       \u001b[1m]\u001b[0m, \u001b[1m[\u001b[0m\u001b[1;36m1.5\u001b[0m       \u001b[1;36m1.5\u001b[0m       \u001b[1;36m5\u001b[0m.        \u001b[1;36m5\u001b[0m.        \u001b[1;36m3.1415927\u001b[0m \u001b[1;36m5\u001b[0m.        \u001b[1;36m1\u001b[0m.\n \u001b[1;36m1\u001b[0m.       \u001b[1m]\u001b[0m, \u001b[1m(\u001b[0m\u001b[1;36m8\u001b[0m,\u001b[1m)\u001b[0m, float32\u001b[1m)\u001b[0m\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">observation_space:\n         <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Box</span><span style=\"font-weight: bold\">([</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-1.5</span>       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-1.5</span>       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-5</span>.        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-5</span>.        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-3.1415927</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-5</span>.\n <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0</span>.        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-0</span>.       <span style=\"font-weight: bold\">]</span>, <span style=\"font-weight: bold\">[</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.5</span>       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.5</span>       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span>.        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span>.        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1415927</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span>.        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>.\n <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>.       <span style=\"font-weight: bold\">]</span>, <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span>,<span style=\"font-weight: bold\">)</span>, float32<span style=\"font-weight: bold\">)</span>\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"action_space:\n         \u001b[1;35mDiscrete\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1m)\u001b[0m\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">action_space:\n         <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Discrete</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span><span style=\"font-weight: bold\">)</span>\n</pre>\n"},"metadata":{}},{"name":"stdout","text":"[ LunarLander-v2 ](state: (8,),action: 4(离散 ))\nSet env random_seed = 543\n","output_type":"stream"},{"name":"stderr","text":"[ 1203/1200 ]: 100%|██████████| 1203/1203 [24:33<00:00,  1.23s/it, Total=175.9, Recent=226.4, RecentBest=263.3, Final=33.4, Steps=281.3] \n","output_type":"stream"}]},{"cell_type":"markdown","source":"### 训练结果查看","metadata":{}},{"cell_type":"code","source":"fig, axes = plt.subplots(1, 2, figsize=(16, 4))\naxes[0].plot(avg_total_rewards, label='Total Rewards')\naxes[0].plot(avg_final_rewards, label='Final Rewards')\naxes[0].set_title(f'{env_name} - Rewards Curve')\naxes[0].legend()\naxes[1].plot(avg_total_step, label='steps')\naxes[1].set_title(f'{env_name} - Steps Curve')\naxes[1].legend()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-14T16:01:43.773940Z","iopub.execute_input":"2023-06-14T16:01:43.774837Z","iopub.status.idle":"2023-06-14T16:01:44.443213Z","shell.execute_reply.started":"2023-06-14T16:01:43.774801Z","shell.execute_reply":"2023-06-14T16:01:44.442470Z"},"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1600x400 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"## &#x2728; Testing\n测试结果将是5次测试的平均奖励","metadata":{}},{"cell_type":"code","source":"agent = ActorCritic(\n    state_dim=env.observation_space.shape[0], \n    action_dim=env.action_space.n, \n    actor_lr=0.0025,\n    critic_lr=0.0025,\n    gamma=0.99,\n    normalize_reward=True,\n    schedule_kwargs=dict(\n        schedule_func_name='LinearLR', \n        end_factor = 0.1,\n        total_iters = 1000\n     )\n)\n\nenv_name= 'LunarLander-v2'\nenv = gym.make(env_name)\nall_seed(seed=NOTEBOOK_SEED, env=env)\nagent.load_model('./check_point_LunarLander-ActorCritic2')\nagent.eval()\ntest_total_reward, action_list = test_agent(agent, env, max_steps=300, plot_flag=True)\nprint(test_total_reward)\nprint(np.mean(test_total_reward))","metadata":{"execution":{"iopub.status.busy":"2023-06-14T16:21:01.851712Z","iopub.execute_input":"2023-06-14T16:21:01.852162Z","iopub.status.idle":"2023-06-14T16:29:17.197607Z","shell.execute_reply.started":"2023-06-14T16:21:01.852129Z","shell.execute_reply":"2023-06-14T16:29:17.196437Z"},"trusted":true},"execution_count":34,"outputs":[{"name":"stdout","text":"[ 5/5 ] step: 301\n165.07581201793346\n[6873.145001689872, 5151.680357221657, 105.92392417548157, 63.27319724993232, 165.07581201793346]\n2471.8196584709754\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"env_name= 'LunarLander-v2'\nenv = gym.make(env_name)\nall_seed(seed=NOTEBOOK_SEED, env=env)\nagent.load_model('./check_point_LunarLander-ActorCritic2')\nagent.eval()\ntest_total_reward, action_list = test_agent(agent, env, max_steps=300, plot_flag=False)\nprint(test_total_reward)\nprint(np.mean(test_total_reward))","metadata":{"execution":{"iopub.status.busy":"2023-06-14T16:35:35.392418Z","iopub.execute_input":"2023-06-14T16:35:35.393731Z","iopub.status.idle":"2023-06-14T16:35:36.830827Z","shell.execute_reply.started":"2023-06-14T16:35:35.393686Z","shell.execute_reply":"2023-06-14T16:35:36.829548Z"},"trusted":true},"execution_count":46,"outputs":[{"name":"stdout","text":"Set env random_seed = 543\nInintial rewards=0\n[ 1/5 ] step: 301 reward: 6873.145001689872\nInintial rewards=0\n[ 2/5 ] step: 301 reward: 5151.680357221657\nInintial rewards=0\n[ 3/5 ] step: 301 reward: 105.92392417548157\nInintial rewards=0\n[ 4/5 ] step: 301 reward: 63.27319724993232\nInintial rewards=0\n[ 5/5 ] step: 301 reward: 165.07581201793346\n[6873.145001689872, 5151.680357221657, 105.92392417548157, 63.27319724993232, 165.07581201793346]\n2471.8196584709754\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# 参考\n以下是一些有用的提示，你可以获得高分。\n\n- [DRL Lecture 1: Policy Gradient (Review)](https://youtu.be/z95ZYgPgXOY)\n- [ML Lecture 23-3: Reinforcement Learning (including Q-learning) start at 30:00](https://youtu.be/2-JNBzCq77c?t=1800)\n- [Lecture 7: Policy Gradient, David Silver](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/pg.pdf)\n\n更多强化学习的学习资料推荐\n- [《Easy RL强化学习教程》](https://datawhalechina.github.io/easy-rl/)\n    - [github](https://github.com/datawhalechina/easy-rl)\n- [《动手学强化学习》](https://hrl.boyuai.com/chapter/)\n\n","metadata":{}},{"cell_type":"code","source":"  ","metadata":{},"execution_count":null,"outputs":[]}]}